Computerized Trading: Maximizing Day Trading and Overnight ProfitsNew York Institute of Finance, 1999 - 415 páginas Discover the answers to all your computerized trading questions, from basic to advanced, in this ground-breaking new guide to successful day trading. Twenty top experts reveal their techniques and strategies for successful computerized trading in this practical guide. |
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Página 74
... expected return equation : Net Expected Return per trade = ( Avg gain × Win odds ) – ( Avg loss × Loss odds ) - - Slippage Commission — Assuming win odds and loss odds at 50 percent each and avg gain equals half the 13.625 pt range ...
... expected return equation : Net Expected Return per trade = ( Avg gain × Win odds ) – ( Avg loss × Loss odds ) - - Slippage Commission — Assuming win odds and loss odds at 50 percent each and avg gain equals half the 13.625 pt range ...
Página 210
... expected annual return of 10 percent and an expected maximum drawdown of 20 percent , then an equal asset allocation among these securities would give an expected return of 10 percent ( their average ) and an expected maximum drawdown ...
... expected annual return of 10 percent and an expected maximum drawdown of 20 percent , then an equal asset allocation among these securities would give an expected return of 10 percent ( their average ) and an expected maximum drawdown ...
Página 276
... expected value of the change in the S & P 500 over the next five days is 5.18 . Use Table 16.3 to compute the expected value of what will happen in the next five days . The following formulas show how . Assume that the data is in the ...
... expected value of the change in the S & P 500 over the next five days is 5.18 . Use Table 16.3 to compute the expected value of what will happen in the next five days . The following formulas show how . Assume that the data is in the ...
Contenido
Chapter | 3 |
Quantifying a Markets Upside and Downside Potential | 12 |
Exiting a Market | 76 |
Derechos de autor | |
Otras 16 secciones no mostradas
Términos y frases comunes
apply approach backtesting bars Bollinger Bands breakout buy signal calculated chart coefficient Coefficient of variation congestion contract data mining data vendors datafeed develop DJIA drawdown equity curve evaluation example Exchange exit Exponential Moving Average Figure formula future fuzzy logic genetic algorithms Index input intraday investors linear losing trades loss Louisiana Pacific method momentum money management moving average neural networks nodes nonlinear pricing nontrending number of trades Omega Research optimization options outlier output pattern percent period portfolio position predict problem programs ratio Relative Strength Index risk run-up sell signals simple moving average Statistical Network Steve Fossett stochastic stop T-bond Table Technical Analysis technical indicators techniques tick tion TradeStation trading performance trading strategy trading system trend trendline uptrend variables volatility volume winning trades zone